What is this so called machine learning that has everyone in the market adopting? Machine learning has varying definitions, depending on the authors of books and articles or one's understanding. I like to define machine learning as a branch of artificial intelligence that provides systems with the ability to learn and act like humans, while also improving their learning over time through observations and real-world interactions without being explicitly programmed. In technical terms, it is simply a set of algorithms with the ability to learn, act and adapt autonomously without being explicitly programmed. Machine learning can be applied in various areas in our day-to-day lives.

The Indian Institute of Technology – Banaras Hindu University (IIT-BHU) and Amazon Internet Services have signed a Memorandum of Understanding (MoU) to develop cloud-ready job skills by providing access to the Amazon Web Services (AWS) Educate program, and to help establish an Artificial Intelligence (AI) and Machine Learning (ML) Cloud Research Lab. The Cloud Research Lab will provide students with opportunities to use AWS Cloud technology to pursue research initiatives that focus on AI and ML innovation for India. Under the MoU agreement, IIT-BHU gains access to the resources in the Amazon Web Services (AWS) Educate program and curriculum designed for higher education institutions to incorporate in to their courses. This collaboration will help accelerate cloud-related technical expertise for students and boost their readiness as they prepare to undertake the industry-recognized certification. "As Chairman of the Committee on Leveraging AI for National Missions in Key Sectors, I am acutely aware of the critical need to develop a proficient and robust talent base in the country in core areas such as cloud computing, artificial intelligence, and machine learning applied to problems in various sectors. As IITs are the premier engineering institutes in India, we have an obligation to ensure that our brightest and the most talented students in the country have easy access to best-in-class technology," said Professor Rajeev Sangal, Director, IIT-BHU.

Some 70 percent of companies claim they're using a form of artificial intelligence (A.I.), according to a new report by Constellation Research. That includes machine learning, deep learning, natural language processing, and cognitive computing. But while companies are interested in what A.I. can potentially do for them, many aren't willing to invest massive amounts of money in the endeavor. Some 92 percent of respondents reported overall A.I. budgets of less than $5 million, with 52 percent paying less than $1 million. However, most plan to increase their A.I.-related spending over the next year.

What is this so called machine learning that has everyone in the market adopting? Machine learning has varying definitions, depending on the authors of books and articles or one's understanding. I like to define machine learning as a branch of artificial intelligence that provides systems with the ability to learn and act like humans, while also improving their learning over time through observations and real-world interactions without being explicitly programmed. In technical terms, it is simply a set of algorithms with the ability to learn, act and adapt autonomously without being explicitly programmed. Machine learning can be applied in various areas in our day-to-day lives.

We propose gradient adversarial training, an auxiliary deep learning framework applicable to different machine learning problems. In gradient adversarial training, we leverage a prior belief that in many contexts, simultaneous gradient updates should be statistically indistinguishable from each other. We enforce this consistency using an auxiliary network that classifies the origin of the gradient tensor, and the main network serves as an adversary to the auxiliary network in addition to performing standard task-based training. We demonstrate gradient adversarial training for three different scenarios: (1) as a defense to adversarial examples we classify gradient tensors and tune them to be agnostic to the class of their corresponding example, (2) for knowledge distillation, we do binary classification of gradient tensors derived from the student or teacher network and tune the student gradient tensor to mimic the teacher's gradient tensor; and (3) for multi-task learning we classify the gradient tensors derived from different task loss functions and tune them to be statistically indistinguishable. For each of the three scenarios we show the potential of gradient adversarial training procedure. Specifically, gradient adversarial training increases the robustness of a network to adversarial attacks, is able to better distill the knowledge from a teacher network to a student network compared to soft targets, and boosts multi-task learning by aligning the gradient tensors derived from the task specific loss functions. Overall, our experiments demonstrate that gradient tensors contain latent information about whatever tasks are being trained, and can support diverse machine learning problems when intelligently guided through adversarialization using a auxiliary network.

We address the problem of spatial segmentation of a 2D object in the context of a robotic system for painting, where an optimal segmentation depends on both the appearance of the object and the size of each segment. Since each segment must take into account appearance features at several scales, we take a hierarchical grammar-based parsing approach to decompose the object into 2D segments for painting. Since there are many ways to segment an object the solution space is extremely large and it is very challenging to utilize an exploration based optimization approach like reinforcement learning. Instead, we pose the segmentation problem as an imitation learning problem by using a segmentation algorithm in the place of an expert, that has access to a small dataset with known foreground-background segmentations. During the imitation learning process, we learn to imitate the oracle (segmentation algorithm) using only the image of the object, without the use of the known foreground-background segmentations. We introduce a novel deterministic policy gradient update, DRAG, in the form of a deterministic actor-critic variant of AggreVaTeD, to train our neural network based object parser. We will also show that our approach can be seen as extending DDPG to the Imitation Learning scenario. Training our neural parser to imitate the oracle via DRAG allow our neural parser to outperform several existing imitation learning approaches.

New York, NY--June 19, 2018--A major challenge in current climate prediction models is how to accurately represent clouds and their atmospheric heating and moistening. This challenge is behind the wide spread in climate prediction. Yet accurate predictions of global warming in response to increased greenhouse gas concentrations are essential for policy-makers (e.g. the Paris climate agreement). In a paper recently published online in Geophysical Research Letters (May 23), researchers led by Pierre Gentine, associate professor of earth and environmental engineering at Columbia Engineering, demonstrate that machine learning techniques can be used to tackle this issue and better represent clouds in coarse resolution ( 100km) climate models, with the potential to narrow the range of prediction. "This could be a real game-changer for climate prediction," says Gentine, lead author of the paper, and a member of the Earth Institute and the Data Science Institute.

Few can deny that there will be huge social, political, and economic transformation as a result of AI, robotics, and digital technologies. But there are many uncertainties about how those changes will play out, especially in the critical area of economics. Does it mean the end of work? From a historical perspective, we have been at similar crossroads before, MIT Economics Professor, Daron Acemoglu, said at the MIT IDE Annual Conference on May 24. Speaking about Automation and the Future of Work, Acemoglu said that many 20th-Century economists -- including John Maynard Keynes and Wassily Leontief -- also worried about technological unemployment in the 1930s and 1950s.

As scientific datasets increase in both size and complexity, the ability to label, filter and search this deluge of information has become a laborious, time-consuming and sometimes impossible task, without the help of automated tools. With this in mind, a team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley are developing innovative machine learning tools to pull contextual information from scientific datasets and automatically generate metadata tags for each file. Scientists can then search these files via a web-based search engine for scientific data, called Science Search, that the Berkeley team is building. As a proof-of-concept, the team is working with staff at the Department of Energy's (DOE) Molecular Foundry, located at Berkeley Lab, to demonstrate the concepts of Science Search on the images captured by the facility's instruments. A beta version of the platform has been made available to Foundry researchers.

The trash truck rumbles down the street, and its cameras pour video into the city's data lake. An AI-powered application mines that image data looking for graffiti--and advises whether to dispatch a fully equipped paint crew or a squad with just soap and brushes. Meanwhile, cameras on other city vehicles could feed the same data lake so another application detects piles of trash that should be collected. That information is used by an application to send the right clean-up squad. Citizens, too, can get into the act, by sending cell phone pictures of graffiti or litter to the city for AI-driven processing.